We consider Simultaneous Balancing and Scheduling of Flexible Mixed Model Assembly Lines with Sequence-Dependent Setup Times (SBSFMMAL-SDST). We propose alternate Mixed Integer Programming (MIP) and Constraint Programming (CP) formulations. Our experiments show that while the MIP models could not solve relatively small instances, the CP approach seems more promising.

In this paper recently developed mixed-integer programming (MIP) tools to the problem of optimal siting and sizing of distributed generators in a distribution network. Here three methodologies for solving the DGPP via mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP) approaches are compared. The single MILP approach uses the well-known DC linear approximati...

Despite existing various integer programming for sequencing problems, there is not enoughinformation about practical values of the models. This paper considers the problem of minimizing maximumlateness with release dates and presents four different mixed integer programming (MIP) models to solve thisproblem. These models have been formulated for the classical single machine problem, namely sequ...

Both integer programming models and heuristic algorithms have been proposed for finding minimum-energy broadcast and multicast trees in wireless ad hoc networks. Among heuristic algorithms, the broadcast/multicast incremental power (BIP/MIP) algorithm is most known. The theoretical performance of BIP/MIP has been quantified in several studies. To assess the empirical performance of BIP/MIP and ...

Both integer programming models and heuristic algorithms have been proposed for finding minimum-energy broadcast and multicast trees in wireless ad hoc networks. Among heuristic algorithms, the broadcast/multicast incremental power (BIP/MIP) algorithm is most known. The theoretical performance of BIP/MIP has been quantified in several studies. To assess the empirical performance of BIP/MIP and ...

Mixed integer programming (MIP) is a powerful modelling tool for decision-making in the industry and in the public sector. Integer requirements are essential to model a wide variety of situations involving assignment restrictions, logical constraints and yes/no decisions, to name a few. Usually real-life applications result in mixed integer programs that are large in size and that are beyond th...

The Chance-Constrained Stochastic Programming (CCSP) is one of the models for decision making under uncertainty. In this paper, we consider the special case of the CCSP in which only the righthand side vector is random with a discrete distribution having a finite support. The unit commitment problem is one of the applications of the special case of the CCSP. Existing methods for exactly solving...

Despite the success of constraint programming (CP) for scheduling, the much wider penetration of mixed integer programming (MIP) technology into business applications means that many practical scheduling problems are being addressed with MIP, at least as an initial approach. Furthermore, there has been impressive and well-documented improvements in the power of generic MIP solvers over the past...

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1]. They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed ...

Given a feasible solution to a Mixed Integer Programming (MIP) model, a natural question is whether that solution can be improved using local search techniques. Local search has been applied very successfully in a variety of other combinatorial optimization domains. Unfortunately, local search relies extensively on the notion of a solution neighborhood, and this neighborhood is almost always ta...